Impact of atmospheric conditions on photovoltaic generation: investigating the influence of aerosols on a grid-connected system
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Oliveira, N. Neves de, B. Neves de Campos, J. Basso Marques, R. da Silva Palácios, and T. Amorim dos Santos. “Impact of Atmospheric Conditions on Photovoltaic Generation: Investigating the Influence of Aerosols on a Grid-Connected System.” International Journal of Energy and Water Resources 10, no. 1 (2026): 46. https://doi.org/10.1007/s42108-025-00468-3.
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Abstract
The growing demand for electrical energy drives the transition toward renewable sources, with a focus on solar photovoltaic energy due to its wide availability and decreasing costs. However, the efficiency of photovoltaic systems is vulnerable to atmospheric factors, such as the presence of aerosols, which affect the atmosphere's radiative balance. This study investigated the impact of aerosol load on the energy generation of a grid-connected photovoltaic system located in the Serra de São Vicente, approximately 70 km from Cuiabá, Mato Grosso, in the Brazilian Cerrado, an area with a high occurrence of seasonal wildfires. Using instantaneous power data from one of the system's inverters and Aerosol Optical Depth (AOD) data from the AERONET network between January and June 2023, the analysis quantified the system's performance variation on days with different aerosol loads (AOD ≤ 0.1 for clean days and AOD > 0.1 for days with significant or high atmospheric loading). The results demonstrated that an elevated presence of aerosols significantly reduces energy generation, especially during peak irradiance hours (10:00–13:00), with power losses reaching 22% at 10:00. The average power loss during the central hours (10:00, 11:00, and 12:00) was approximately 12.9%, suggesting that the attenuation of direct radiation by aerosols is not compensated by the increase in diffuse radiation. The study concludes that AOD monitoring is potentially relevant for planning and operation of solar power plants in tropical regions with high atmospheric variability, enhancing generation predictability and the management of photovoltaic systems.
